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Part 1: GIS, GPS, RS and Precision FarmingInternational audienceHyperspectral reflectance of normal and lodged rice caused by rice brown planthopper and rice panicle blast was measured at the canopy level. Over one decade broad- and narrow-band vegetation indices (VIs) were calculated to simulate Landsat ETM+ with in situ hyperspectral reflectance. Principal component analysis (PCA) was utilized to obtain the front two principal components (PCs). Probabilistic neural network (PNN) was employed to classify the lodged and normal rice with VIs and PCs as the input vectors. PCs had 100% of overall accuracy and 1 of Kappa coefficient for the training dataset. While PCs had the greatest average overall accuracy (97.8%) and Kappa coefficient (0.955) for the two testing datasets than VIs consisting of broad- and narrow-bands. The results indicated that hyperspectral remote sensing with PCA and artificial neural networks could potentially be applied to discriminate lodged crops from normal ones at regional and large spatial scales

Part 1: GIS, GPS, RS and Precision FarmingInternational audienceWe designed and implemented an integration framework of enterprise business information and multi-source heterogeneous geospatial information based on composition of semantic geospatial Web service. It can integrate business information and geospatial information seamlessly and effectively in enterprise workflow. An example of tobacco planting spatial analysis this framework was described to verify the practicability and feasibility of this framework

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